## Note2. Convex Optimization Problem 02.

This entry is part 2 of 10 in the series ConvexOptimization

Note2. [PDF Link] Below notes were taken by my iPad Pro 3.0 and exported to PDF files. All contents were based on “Optimization for AI (AI505)” lecture notes at KAIST. For the supplements, lecture notes from Martin Jaggi [link] and “Convex Optimization” book of Sebastien Bubeck [link] were used. If you will have found any license issue, …

## Note1. Convex Optimization Problem 01.

This entry is part 1 of 10 in the series ConvexOptimization

Note1. [PDF Link] Below notes were taken by my iPad Pro 3.0 and exported to PDF files. All contents were based on “Optimization for AI (AI505)” lecture notes at KAIST. For the supplements, lecture notes from Martin Jaggi [link] and “Convex Optimization” book of Sebastien Bubeck [link] were used. If you will have found any license issue, …

## MIT 18.06 Linear Algebra – Basics (Lec01~Lec03)

I’ve written this post based on ‘Linear Algebra’ lectures (MIT 18.06 from lecture-01 to lecture-03) by Gilbert Strang. This post is intended for person who needs to learn basic knowledge about linear algebra. (YouTube link for Strang’s Linear Algebra : Here ) At this post, I used One-Note (Microsoft) program replacing original hand-written notes. So, …

## ISLR chapter 03. Linear Regression_3.1 Simple Linear Regression

From below I’ve quoted some paragraphs from page 59~page 70  directly in the ISLR book.   Recall the Advertising data from Chapter 2. The figure below displays sales for a particular product as a function of advertising budgets for TV, radio, and newspaper media. Suppose that in our role as statistical consultants we are asked …

## ISLR chapter 02. Statistical Learning

2.1 What Is Statistical Learning? X : input variables; predictors; independent variables Y : output variables; response; dependent variable Suppose that we observe a quantitative response Y and p different predictors, . We assume that there is some relationship between Y and 《Very general form》   2.1.1 Why Estimate f ? ① Prediction We can …

## ISLR chapter 01. Introduction

A Brief History of Statistical Learning Though the term statistical learning is fairly new, many of the concepts that underlie the field were developed long ago. At the beginning of the nineteenth century, Legendre and Gauss published papers on the method of least squares, which implemented the first successfully applied to problems in astronomy. Linear …